Type of Document Master's Thesis Author Love, Randall James URN etd-06112009-063248 Title Predictive software design measures Degree Master of Science Department Computer Science Advisory Committee
Advisor Name Title Nance, Richard E. Committee Chair Arthur, James D. Committee Member Henry, Sallie M. Committee Member Keywords
- Computer software
Date of Defense 1994-10-05 Availability restricted Abstract
This research develops a set of predictive measures enabling software testers and designers to identify and target potential problem areas for additional and/or enhanced testing. Predictions are available as early in the design process as requirements allocation and as late as code walk-throughs. These predictions are based on characteristics of the design artifacts prior to coding.
Prediction equations are formed at established points in the software development process called milestones. Four areas of predictive measurement are examined at each design milestone for candidate predictive metrics. These areas are: internal corrlplexity, information flow, defect categorization, and the change in design. Prediction equations are created from the set of candidate predictive metrics at each milestone. The most promising of the prediction equations are selected and evaluated. The single "best" prediction equation is selected at each design milestone.
The resulting predictions are promising in terms of ranking areas of the software design by the number of predicted defects. Predictions of the actual number of defects are less accurate.
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